Learning To See – Making deep neural network predictions on live camera input

Latest in the series of experiments and explorations into neural networks by Memo Akten is a pre-trained deep neural network able to make predictions on live camera input – trying to make sense of what it sees, in context of what it’s seen before.

Learning To See is an ongoing series of works that use state-of-the-art Machine Learning algorithms as a means of reflecting on ourselves and how we make sense of the world. The picture we see in our conscious minds is not a direct representation of the outside world, or of what our senses deliver, but is of a simulated world, reconstructed based on our expectations and prior beliefs. Artificial neural networks loosely inspired by our own visual cortex look through surveillance cameras and try to make sense of what they are seeing. Of course they can see only what they already know. Just like us.

The network is trained on tens of thousands of images scraped from the Google Art Project, containing scans from art collections and museums from all over the world. These include paintings, illustrations, sketches and photographs covering landscapes, portraits, religious imagery, pastoral scenes, maritime scenes, scientific illustrations, prehistoric cave paintings, abstract images, cubist, realist paintings and many more.

The work is part Memo Akten’s broader line of inquiry about self affirming cognitive biases, our inability to see the world from others’ point of view, and the resulting social polarisation.